Instructions to use Jingbiao/Qwen2-VL-7B-Harm-P-LMM-RGCL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jingbiao/Qwen2-VL-7B-Harm-P-LMM-RGCL with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="Jingbiao/Qwen2-VL-7B-Harm-P-LMM-RGCL")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Jingbiao/Qwen2-VL-7B-Harm-P-LMM-RGCL", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Jingbiao/Qwen2-VL-7B-Harm-P-LMM-RGCL with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jingbiao/Qwen2-VL-7B-Harm-P-LMM-RGCL"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Jingbiao/Qwen2-VL-7B-Harm-P-LMM-RGCL",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Jingbiao/Qwen2-VL-7B-Harm-P-LMM-RGCL

SGLang

How to use Jingbiao/Qwen2-VL-7B-Harm-P-LMM-RGCL with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Jingbiao/Qwen2-VL-7B-Harm-P-LMM-RGCL" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Jingbiao/Qwen2-VL-7B-Harm-P-LMM-RGCL",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Jingbiao/Qwen2-VL-7B-Harm-P-LMM-RGCL" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Jingbiao/Qwen2-VL-7B-Harm-P-LMM-RGCL",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use Jingbiao/Qwen2-VL-7B-Harm-P-LMM-RGCL with Docker Model Runner:
```
docker model run hf.co/Jingbiao/Qwen2-VL-7B-Harm-P-LMM-RGCL
```

Improve model card: Add details, links, and pipeline tag

by nielsr HF Staff - opened Sep 10, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+31

-208

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README.md +31 -208

README.md CHANGED Viewed

@@ -1,234 +1,57 @@
 ---
 base_model: QWen/QWen2-VL-7B-Instruct
-library_name: peft
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
 ## Model Details
 ### Model Description
-<!-- Provide a longer summary of what this model is. -->
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
 ## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 ### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
 ### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 ## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]
-### Framework versions
-- PEFT 0.12.0
-```
-@inproceedings{RGCL2024Mei,
-    title = "Improving Hateful Meme Detection through Retrieval-Guided Contrastive Learning",
-    author = "Mei, Jingbiao  and
-      Chen, Jinghong  and
-      Lin, Weizhe  and
-      Byrne, Bill  and
-      Tomalin, Marcus",
-    editor = "Ku, Lun-Wei  and
-      Martins, Andre  and
-      Srikumar, Vivek",
-    booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
-    month = aug,
-    year = "2024",
-    address = "Bangkok, Thailand",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/2024.acl-long.291",
-    doi = "10.18653/v1/2024.acl-long.291",
-    pages = "5333--5347"
 }
- @article{RAHMD2025Mei, title={Robust Adaptation of Large Multimodal Models for Retrieval Augmented Hateful Meme Detection},
-          url={http://arxiv.org/abs/2502.13061},
-          DOI={10.48550/arXiv.2502.13061},
-          note={arXiv:2502.13061 [cs]},
-          number={arXiv:2502.13061},
-          publisher={arXiv},
-          author={Mei, Jingbiao and Chen, Jinghong and Yang, Guangyu and Lin, Weizhe and Byrne, Bill},
-          year={2025},
-          month=may }
 ```

 ---
 base_model: QWen/QWen2-VL-7B-Instruct
+library_name: transformers
+license: apache-2.0
+pipeline_tag: image-text-to-text
 ---
+# Robust Adaptation of Large Multimodal Models for Retrieval Augmented Hateful Meme Detection
+This repository contains the RA-HMD model presented in the paper [Robust Adaptation of Large Multimodal Models for Retrieval Augmented Hateful Meme Detection](https://huggingface.co/papers/2502.13061).
 ## Model Details
 ### Model Description
+RA-HMD proposes a robust adaptation framework for hateful meme detection that enhances in-domain accuracy and cross-domain generalization while preserving the general vision-language capabilities of LMMs. It achieves improved robustness under adversarial attacks compared to SFT models and demonstrates state-of-the-art performance across various meme classification datasets. Additionally, RA-HMD generates higher-quality rationales for explaining hateful content, enhancing model interpretability.
+- **Developed by:** Jingbiao Mei, Jinghong Chen, Guangyu Yang, Weizhe Lin, Bill Byrne
+- **Model type:** Fine-tuned QWen2-VL-7B-Instruct using PEFT (LoRA)
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0
+- **Finetuned from model:** `QWen/QWen2-VL-7B-Instruct`
+### Model Sources
+- **Repository:** https://github.com/JingbiaoMei/RGCL
+- **Paper:** https://huggingface.co/papers/2502.13061
+- **Project page:** https://rgclmm.github.io/
 ## Uses
 ### Direct Use
+The model is intended for robust hateful meme detection and generating explanatory rationales.
 ### Out-of-Scope Use
+This model is specifically trained for hateful meme detection. Using it for general image captioning or unrelated classification tasks may lead to suboptimal results.
 ## How to Get Started with the Model
+Refer to the [GitHub repository](https://github.com/JingbiaoMei/RGCL) for detailed installation and usage instructions. The RA-HMD Stage 1 code is released as a submodule in [LLaMA-Factory@a88f610](https://github.com/JingbiaoMei/LLaMA-Factory-LMM-RGCL/tree/a88f610e9fa46d1ef1669c5dbc39ee9008f95c21).
+## Citation
+If our work helped your research, please kindly cite our paper:
+```bibtex
+@article{RAHMD2025Mei,
+  title={Robust Adaptation of Large Multimodal Models for Retrieval Augmented Hateful Meme Detection},
+  url={http://arxiv.org/abs/2502.13061},
+  DOI={10.48550/arXiv.2502.13061},
+  note={arXiv:2502.13061 [cs]},
+  number={arXiv.2502.13061},
+  publisher={arXiv},
+  author={Mei, Jingbiao and Chen, Jinghong and Yang, Guangyu and Lin, Weizhe and Byrne, Bill},
+  year={2025},
+  month=may
 }
 ```